@inproceedings{ ISI:000343655300141,
Author = {Yang, Zhaojun and Metallinou, Angeliki and Erzin, Engin and Narayanan,
   Shrikanth},
Book-Group-Author = {{IEEE}},
Title = {{ANALYSIS OF INTERACTION ATTITUDES USING DATA-DRIVEN HAND GESTURE PHRASES}},
Booktitle = {{2014 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL
   PROCESSING (ICASSP)}},
Series = {{International Conference on Acoustics Speech and Signal Processing
   ICASSP}},
Year = {{2014}},
Note = {{IEEE International Conference on Acoustics, Speech and Signal Processing
   (ICASSP), Florence, ITALY, MAY 04-09, 2014}},
Organization = {{IEEE}},
Abstract = {{Hand gesture is one of the most expressive, natural and common types of
   body language for conveying attitudes and emotions in human
   interactions. In this paper, we study the role of hand gesture in
   expressing attitudes of friendliness or conflict towards the
   interlocutors during interactions. We first employ an unsupervised
   clustering method using a parallel HMM structure to extract recurring
   patterns of hand gesture (hand gesture phrases or primitives). We
   further investigate the validity of the derived hand gesture phrases by
   examining the correlation of dyad's hand gesture for different
   interaction types defined by the attitudes of interlocutors. Finally, we
   model the interaction attitudes with SVM using the dynamics of the
   derived hand gesture phrases over an interaction. The classification
   results are promising, suggesting the expressiveness of the derived hand
   gesture phrases for conveying attitudes and emotions.}},
ISSN = {{1520-6149}},
ISBN = {{978-1-4799-2893-4}},
ResearcherID-Numbers = {{Erzin, Engin/H-1716-2011}},
ORCID-Numbers = {{Erzin, Engin/0000-0002-2715-2368}},
Unique-ID = {{ISI:000343655300141}},
}

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In this paper, we study the role of hand gesture in expressing attitudes of friendliness or conflict towards the interlocutors during interactions. We first employ an unsupervised clustering method using a parallel HMM structure to extract recurring patterns of hand gesture (hand gesture phrases or primitives). We further investigate the validity of the derived hand gesture phrases by examining the correlation of dyad's hand gesture for different interaction types defined by the attitudes of interlocutors. Finally, we model the interaction attitudes with SVM using the dynamics of the derived hand gesture phrases over an interaction. The classification results are promising, suggesting the expressiveness of the derived hand gesture phrases for conveying attitudes and emotions.","issn":"1520-6149","isbn":"978-1-4799-2893-4","researcherid-numbers":"Erzin, Engin/H-1716-2011","orcid-numbers":"Erzin, Engin/0000-0002-2715-2368","unique-id":"ISI:000343655300141","bibtex":"@inproceedings{ ISI:000343655300141,\nAuthor = {Yang, Zhaojun and Metallinou, Angeliki and Erzin, Engin and Narayanan,\n Shrikanth},\nBook-Group-Author = {{IEEE}},\nTitle = {{ANALYSIS OF INTERACTION ATTITUDES USING DATA-DRIVEN HAND GESTURE PHRASES}},\nBooktitle = {{2014 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL\n PROCESSING (ICASSP)}},\nSeries = {{International Conference on Acoustics Speech and Signal Processing\n ICASSP}},\nYear = {{2014}},\nNote = {{IEEE International Conference on Acoustics, Speech and Signal Processing\n (ICASSP), Florence, ITALY, MAY 04-09, 2014}},\nOrganization = {{IEEE}},\nAbstract = {{Hand gesture is one of the most expressive, natural and common types of\n body language for conveying attitudes and emotions in human\n interactions. In this paper, we study the role of hand gesture in\n expressing attitudes of friendliness or conflict towards the\n interlocutors during interactions. We first employ an unsupervised\n clustering method using a parallel HMM structure to extract recurring\n patterns of hand gesture (hand gesture phrases or primitives). We\n further investigate the validity of the derived hand gesture phrases by\n examining the correlation of dyad's hand gesture for different\n interaction types defined by the attitudes of interlocutors. Finally, we\n model the interaction attitudes with SVM using the dynamics of the\n derived hand gesture phrases over an interaction. 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